Canadian and Spanish Youth 1 In press: International Journal of Psychology Running Head: DRINKING AND DRIVING Canadian and Spanish Youths ’ Risk Perceptions of Drinking and Driving and Riding with a Drunk
Bibliographic record
Abstract
The present research compared Canadian and Spanish youths ’ perceptions of the potential benefits and drawbacks of drinking under the influence of alcohol (DUI) and riding with a drunk driver (RDD). Eighty (41 female) Canadian and 87 (71 female) Spanish undergraduates completed a survey asking about their past and forecasted engagement in DUI and RDD, and their perceptions of the benefits and drawbacks of DUI and RDD. A sizeable proportion of both samples reported DUI and RDD in the past year. Past risk takers forecasted significantly greater chances of engaging in these behaviors in the following year compared to those who had not engaged in DUI and RDD. Both samples provided significantly more drawbacks than benefits of DUI and RDD. Whereas the benefits of both behaviors tended to refer to personal effects (e.g., save money, arrive faster) that occurred before, during or after driving, the drawbacks referred to a range of outcomes (e.g., accident, kill/injure, penal sanction) that mostly occurred during driving. Although Canada and Spain differ in important respects (e.g., potential penalty for DUI), there were similarities in the two samples ’ perceptions of DUI and RDD. Young people are aware of the costs of these risky
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".